Während die einen an Schicksalsschlägen zerbrechen, wachsen andere daran. Was ist deren Geheimnis? Dieser Frage spüren Forscher unterschiedlichster Disziplinen nach. Ihre Ergebnisse machen vor allem: Mut.
.... and Forschungzentrum Jülich is involved in the project
The activity was modelled using a supercomputer with 1.4 millino GB of RAM and 700,000 processor cores
A powerful supercomputer in Japan has broken the code of the human brain, accurately mapping one second of one percent of human brain activity.
The numbers may not sound too impressive, but this is an important breakthrough in simulation technology and has wide-reaching implications.
Using the K supercomputer, the fourth most powerful in world, scientists in Japan replicated a network of 1.73 billion nerve cells and 10.4 trillion synapses.
It took the K computer, with over 700,000 processor cores and 1.4 million GB of RAM, 40 minutes to model the data.
The most sophisticated of its kind, the project, a joint venture between Japanese research group RIKEN and German research group Forschungszentrum Jülich, was designed to gauge the limits of brain simulation technology.
RIKEN’s success is the most significant development in the global brain race, wherein the world’s major economies are racing to map the human brain and unlock its secrets.
The USA has the BRAIN initiative, the EU has The Human Brain Project, and China has Brainnetome.
But mapping and then simulating the human brain requires next-gen supercomputing, an order of computational power known as 'exascale'.
An exascale computer is one that can perform a quintillion floating point operations per second, thought to be same power as a human brain.
No computer as powerful as this yet exists, but as well as these major nations and international organizations, private companies are also developing exascale technology.
Intel, for instance, has said that it aims to have this future-computer operational by 2018.
“If petascale computers like the K computer are capable of representing one per cent of the network of a human brain today, then we know that simulating the whole brain at the level of the individual nerve cell and its synapses will be possible with exascale computers - hopefully available within the next decade,” Markus Diesmann, one of the scientists involved, told the Daily Telegraph.
Such technology would herald an age of sophisticated medical research, and intelligent defence weaponry, and even artificial intelligence.
The Most Powerful Supercomputers in the World:
National Super Computer Centre in Guangzhou (China)
Oak Ridge National Laboratory (Tennessee, USA)
National Nuclear Security Association (Nevada, USA)
RIKEN Advanced Institute for Computational Science (Japan)
Argonne National Laboratory (Illinois, USA)
" Machine learning, in many of its forms, is about building programs that themselves build programs. But these machine-generated programs—neural networks, Bayesian belief networks,evolutionary algorithms—are nothing like human-generated algorithms. Instead of being programmed, they are “trained” by their designers through an iterative process of providing positive and negative feedback on the results they give. They are difficult (sometimes impossible) to understand, tricky to debug, and harder to control. Yet it is precisely for these reasons that they offer the potential for far more “intelligent” behavior than traditional approaches to algorithms and A.I."
This is a great interview with the University of Washington's Pedro Domingos about his new book, The Master Algorithm. I bought the book the other day, but have yet to dive in. This article made me excited to do so.
Domingos divides the field into five contemporary machine-learning paradigms—evolutionary algorithms, connectionism and neural networks, symbolism, Bayes networks, and analogical reasoning—which he imagines being unified in one future “master algorithm” capable of learning nearly anything.
On the differences between machine learning and the way coding is done today:
The question, then, is why one would want to generate opaque and unpredictable networks rather than writing strict, effective programs oneself. The answer, as Domingos told me, is that “complete control over the details of the algorithm doesn’t scale.” There are three related aspects to machine learning that mitigate this problem:
(1) It uses probabilities rather than the true/false binary.
(2) Humans accept a loss of control and precision over the details of the algorithm.
(3) The algorithm is refined and modified through a feedback process.
This one's a worthwhile read.
Tätigkeiten, die auf Feinmotorik, Verhandlungsgeschick und Urteilsvermögen basieren, bleiben weiterhin gefragt.
- Teaching Psychology, German and Theory of Knowledgepresent
- Deutsche Schule TokyoHigh School
- RWTH AachenUniversity
- University of BonnMasters Degree, German Literature
- University of OsloPsychology
- University of BodøMultimedia journalism, 2011
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